A ranking-based approach for hierarchical classification

With the growth of information organized in hierarchical databases, it is essential to develop automated approaches for classifying data instances (e.g., documents, proteins and images) into hierarchies. Several classification approaches have been developed that exploit the hierarchical structure prevalent within these underlying databases. One commonly used approach is to train local one-versus-rest classifiers for each of the nodes within the hierarchy and then make a prediction using a combination of these several trained classifiers. In this paper, we develop a hierarchical classification approach that utilizes a rank-based loss function to differentiate between the examples from different nodes across the hierarchies. The intuition behind our approach is that it allows for the training examples to have an ordering based on their class membership within the hierarchy. We also add regularization constraints that force the learned weight vectors associated with the parent and children nodes within the hierarchy to be similar to each other. To further improve the model performance, we extend our formulation to include a hierarchy based cost-sensitive loss during training. To ensure the scalability of our approach to datasets with large number of classes, instances and feature space, we implemented a distributed map-reduce based algorithm for training the different per-node classifiers. Our empirical results on a diverse set of image and text hierarchical databases shows an improved performance with the use of rank-based models in comparison to other baseline approaches.